CN114496198A - Smart city vaccine scheduling method and system based on Internet of things - Google Patents

Smart city vaccine scheduling method and system based on Internet of things Download PDF

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Publication number
CN114496198A
CN114496198A CN202210353121.9A CN202210353121A CN114496198A CN 114496198 A CN114496198 A CN 114496198A CN 202210353121 A CN202210353121 A CN 202210353121A CN 114496198 A CN114496198 A CN 114496198A
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vaccine
vaccination
information
service
platform
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CN114496198B (en
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邵泽华
权亚强
梁永增
向海堂
温志惠
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Chengdu Qinchuan IoT Technology Co Ltd
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Chengdu Qinchuan IoT Technology Co Ltd
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Priority to US17/661,275 priority patent/US11756678B1/en
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
    • G06Q10/1093—Calendar-based scheduling for persons or groups
    • G06Q10/1095—Meeting or appointment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00—Economic sectors
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/30—Control

Abstract

The embodiment of the specification provides a smart city vaccine scheduling method and system based on the Internet of things, wherein the smart city vaccine scheduling method is executed by a city vaccine management platform and comprises the following steps: acquiring a service range corresponding to each vaccination point in a preset area; obtaining relevant information of vaccine service in a service range, wherein the relevant information of the vaccine service comprises vaccination information, target vaccinator information and time types of all preset time points; and predicting based on the vaccine service related information of the service range, and determining a prediction result. The prediction result comprises the possible vaccination population of each vaccination point at each preset time point; based on the prediction results, a vaccine distribution schedule is determined.

Description

Smart city vaccine scheduling method and system based on Internet of things
Technical Field
The specification relates to the field of information management, in particular to a smart city vaccine scheduling method and system based on the Internet of things.
Background
The vaccine injection is concerned with the livelihood, and particularly, under the condition that large-scale epidemic situations occur, the vaccination points of hospitals, community service sites and the like in cities are faced with a large number of vaccinees to inject the vaccine. This presents challenges to the solution of how to schedule the number of vaccines at each vaccination site, and to schedule vaccinators reasonably to avoid crowding the vaccinated population.
The internet of things is an important component of a new generation of information technology, is an extended and expanded network on the basis of the internet, combines various information sensing devices with the network to form a huge network, and realizes interconnection and intercommunication of people, machines and things at any time and any place. The method provides a technical foundation for cities with dense population and large vaccination demand. Through the internet of things technology, the vaccine scheduling management system based on the internet of things technology is designed to be of great significance. Therefore, the method for efficiently scheduling the vaccine and reasonably arranging the vaccination plan is provided by combining the technology of the Internet of things.
Disclosure of Invention
One or more embodiments of the present specification provide a smart city vaccine scheduling method based on the internet of things. The method is performed by a municipal vaccine management platform, the method comprising: the method is performed by a municipal vaccine management platform, the method comprising: acquiring a service range corresponding to each vaccination point in a preset area; obtaining relevant information of the vaccine service in the service range, wherein the relevant information of the vaccine service comprises vaccination information, target vaccinator information and time types of all preset time points; determining a prediction result based on the vaccine service related information of the service range, wherein the prediction result comprises the possible vaccination population of each vaccination point at each preset time point; determining a dispensing regimen for the vaccine based on the prediction.
One or more embodiments of the present specification provide an internet of things-based smart city vaccine scheduling system, the system comprising a user platform, a vaccine service platform, and a city vaccine management platform configured to: acquiring a service range corresponding to each vaccination point in a preset area; acquiring relevant vaccine service information of the service range based on the service range, wherein the relevant vaccine service information comprises vaccination information, target vaccinator information and time types of all preset time points; determining a prediction result based on the vaccine service related information of the service range, wherein the prediction result comprises the possible vaccination population of each vaccination point at each preset time point; determining a dispensing regimen for the vaccine based on the prediction.
One or more embodiments of the present specification provide an internet of things-based smart city vaccine scheduling apparatus, the apparatus including at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least a portion of the computer instructions to implement a method for smart city vaccine scheduling based on internet of things.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes a smart city vaccine scheduling method based on the internet of things.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of a smart city vaccine dispatch system according to some embodiments of the present disclosure;
FIG. 2 is an exemplary schematic diagram of a smart city vaccine dispatch system in accordance with certain embodiments herein;
FIG. 3 is an exemplary flow diagram of a smart city vaccine scheduling method according to some embodiments described herein;
FIG. 4 is an exemplary schematic diagram of a vaccination information query, according to some embodiments of the present description;
FIG. 5 is an exemplary flow diagram illustrating the determination of a prediction result by a predictive model according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
The terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is a schematic diagram of an application scenario of a smart city vaccine dispatch system according to some embodiments of the present disclosure. As shown in fig. 1, an application scenario 100 of the vaccine scheduling system may include a processing device 110, a network 120, a storage device 130, a user terminal 140, and a vaccination site terminal 150.
In some embodiments, the processing device 110, the storage device 130, the user terminal 140, and/or the vaccination point terminal 150 may be connected and/or in communication with each other via the network 120 (e.g., a wireless connection, a wired connection, or a combination thereof). As shown in fig. 1, processing device 110 may be connected to storage device 130 through network 120. As another example, user terminal 140 may be coupled to processing device 110, storage device 130, through network 120.
The processing device 110 may be used to process information and/or data related to the application scenario 100, e.g., prediction results, vaccine distribution schemes, etc. Processing device 110 may process data, information, and/or processing results obtained from other devices or system components and execute program instructions based on the data, information, and/or processing results to perform one or more functions described herein. In some embodiments, the processing device 110 may be configured for platform maintenance and management work for an urban vaccine management platform.
The network 120 may connect the components of the application scenario 100 and/or connect the application scenario 100 with external resource components. The network enables communication between the components and with other components outside of the application scenario 100, facilitating the exchange of data and/or information. The network may be a local area network, a wide area network, the internet, etc., and may be a combination of various network architectures.
Storage device 130 may be used to store data and/or instructions. In some embodiments, storage device 130 may store data and/or instructions for use by processing device 110 in performing or using the exemplary methods described in this specification. In some embodiments, the storage device 130 may be connected to the network 120 to communicate with one or more components of the application scenario 100 (e.g., processing device 110, user terminal 140).
The user terminal 140 may include one or more terminal devices or software. In some embodiments, the user terminal 140 may include a mobile phone 140-1, a tablet 140-2, a laptop computer 140-3, and the like. In some embodiments, a user may view information and/or enter data and/or instructions through a user terminal. For example, the user may input an instruction to query vaccination information through the user terminal. As another example, the user may view vaccination information through the user terminal.
The vaccination site terminal 150 may be a computing device equipped with a site for vaccinating a population of people. In some embodiments, the vaccination point terminal 150 may include, but is not limited to, a computing device of a hospital, community service station, or like vaccination point. In some embodiments, the user may receive services at the vaccination site, including but not limited to: vaccination, counseling, inquiry, etc. In some embodiments, the vaccination point terminal 150 may be connected to the network 120 to communicate with one or more components of the application scenario 100 (e.g., processing device 110, user terminal 140).
It should be noted that the application scenarios are provided for illustrative purposes only and are not intended to limit the scope of the present specification. It will be apparent to those skilled in the art that various modifications and variations can be made in light of the description herein. For example, the application scenario may implement similar or different functionality on other devices. However, variations and modifications may be made without departing from the scope of the present description.
The Internet of things system is an information processing system comprising a management platform, a service platform and a user platform, wherein part or all of the management platform, the service platform and the user platform are arranged. The management platform can realize overall planning and coordination of connection and cooperation among functional platforms (such as a service platform and a user platform). The management platform converges information of the operation system of the Internet of things, and can provide sensing management and control management functions for the operation system of the Internet of things. The service platform can realize the connection between the management platform and the user platform and has the functions of sensing information service communication and controlling information service communication. The user platform is a functional platform for realizing user perception information acquisition and control information generation.
The processing of information in the internet of things system can be divided into a processing flow of user perception information and a processing flow of control information. The control information may be information generated based on user perception information. In some embodiments, the control information may include user demand control information and the user perception information may include user query information. The processing of the user query information is related information such as searching, browsing, uploading and feedback and the like which are acquired by the user platform and carried out on the user platform by the user, and the related information is transmitted to the user platform by the management platform through the service platform. The user requirement control information is issued to the management platform by the user platform through the service platform, and then the requirement service is provided for the corresponding user.
In some embodiments, when the system of internet of things is applied to city management, the system of internet of things can be called a smart city system of internet of things.
Fig. 2 is an exemplary schematic diagram of a smart city vaccine dispatch system in accordance with some embodiments herein. As shown in fig. 2, the smart city vaccine scheduling system 200 may be implemented based on an internet of things system, and the smart city vaccine scheduling system 200 includes a user platform 210, a vaccine service platform 220, and a city vaccine management platform 230. In some embodiments, the smart city vaccine dispatch system 200 may be part of the processing device 110 or implemented by the processing device 110.
In some embodiments, the smart city vaccine dispatch system 200 may be applied in a variety of scenarios for vaccine dispatch. In some embodiments, the smart city vaccine scheduling system 200 may obtain vaccination information under multiple scenes respectively to obtain a vaccine scheduling policy under each scene. In some embodiments, the smart city vaccine scheduling system 200 may obtain a vaccine scheduling policy for an entire area (e.g., an entire city) based on obtaining vaccination information for each scenario.
Various scenarios of vaccine scheduling may include scheduling of vaccine quantities, scheduling of vaccination times, prediction of vaccine scheduling, and the like. It should be noted that the above scenario is only an example, and does not limit the specific application scenario of the smart city vaccine scheduling system 200, and those skilled in the art can apply the smart city vaccine scheduling system 200 to any other suitable scenario based on the disclosure of the present embodiment.
In some embodiments, the smart city vaccine dispatch system 200 may be applied to dispatch vaccine quantities. When applied to scheduling of vaccine amounts, the city vaccine management platform 230 may obtain information about each vaccination point in a certain area (e.g., a city, a certain area in a city, etc.) and the number of people in the area. The vaccination site may be a pre-established hospital, a temporarily established community service vaccination site, or the like. The city vaccine management platform 230 may divide the service range for each vaccination site, for example, set the service radius of the vaccination site, etc., and the larger the service radius, the larger the amount of vaccine distributed. The city vaccine management platform 230 can process the information according to the service range of the vaccination point and the population number in the service range to make strategies or instructions related to the scheduling of the vaccine number, such as the instruction of expanding or contracting the service radius of the vaccination point. The city vaccine management platform 230 uploads the vaccine quantity scheduling policy to the vaccine service platform 220, and the vaccine quantity scheduling policy is uploaded to the user platform 210 by the vaccine service platform 220 and fed back to the user.
In other embodiments, when the smart city vaccine scheduling system 200 is applied to scheduling the number of vaccines, the user platform 210 may collect the query information of the user for the vaccination point, for example, the number of queries of the user for a certain vaccination point through the user terminal device in a certain period of time, the more queried the vaccination point, the higher popular level of the vaccination point, the more number of potential previous vaccinations. The user platform 210 may send the collected query information of the vaccination point to the vaccine service platform 220 as user demand control information. The vaccine service platform 220 further issues the query information to the city vaccine management platform 230, and the city vaccine management platform 230 processes the statistical data of the collected query information and then makes a policy or instruction related to the scheduling of the vaccine quantity, such as the distribution quantity of the additional vaccines. The city vaccine management platform 230 uploads the strategies related to the scheduling of the vaccine quantity to the vaccine service platform 220, and the strategies are uploaded to the user platform 210 by the vaccine service platform 220 and fed back to the user.
In some embodiments, the smart city vaccine scheduling system 200 may be applied to the scheduling of vaccination times. When applied to scheduling of vaccination times, the urban vaccine management platform 230 may obtain demographic information within the service range of the vaccination site, which may include population distributions for various age groups, such as population under 18, 18 to 50, and over 50. Demographic information may also include professional information, such as whether a person is in employment, the industry in which it is located, the place of employment, and the like. The urban vaccine management platform 230 may process based on demographic information to make strategies or instructions related to scheduling vaccination times. For example, vaccinees under 18 years of age and over 50 years of age are scheduled to vaccinate before weekdays (e.g., monday through friday), and vaccinees between 18 years of age and 50 years of age are scheduled to vaccinate before weekends (e.g., saturday or sunday). The city vaccine management platform 230 uploads the vaccination time scheduling policy to the vaccine service platform 220, and the vaccination time scheduling policy is uploaded to the user platform 210 by the vaccine service platform 220 and fed back to the user.
In some embodiments, the smart city vaccine dispatch system 200 may be applied for prediction of vaccine dispatch. When applied to prediction of vaccine scheduling, the urban vaccine management platform 230 may collect data related to scheduling predictions. The data related to scheduling prediction may include epidemic information in the area (e.g., the number of confirmed persons, suspected confirmed persons, severe persons, asymptomatic persons, etc. in the area), the number of persons inoculated and not inoculated in the area, the number of times the inoculation point is queried, etc. The city vaccine management platform 230 may process the information and make strategies or instructions related to vaccine scheduling prediction. For example, the number of vaccinations at each vaccination site is predicted for a future period of time and the distribution of the number of vaccines is made. The city vaccine management platform 230 uploads the prediction result of vaccine scheduling to the vaccine service platform 220, and the prediction result is uploaded to the user platform 210 by the vaccine service platform 220 and fed back to the user.
In some embodiments, the smart city vaccine dispatch system 200 may be comprised of multiple smart city vaccine dispatch subsystems, each of which may be applied to a scenario. The smart city vaccine scheduling system 200 may perform comprehensive management and processing on data acquired and output by each subsystem, thereby obtaining relevant strategies or instructions for assisting smart city vaccine scheduling.
For example, the smart city vaccine dispatch system 200 may include subsystems for vaccine quantity scheduling, vaccination time scheduling, and vaccine schedule prediction, respectively. The smart city vaccine dispatch system 200 serves as the superior system for each subsystem.
The following description will be given by taking the smart city vaccine scheduling system 200 as an example for managing each subsystem and obtaining corresponding data based on the subsystem to obtain the strategy for smart city vaccine scheduling:
the smart city vaccine scheduling system 200 may obtain data such as the number distribution of vaccines at each vaccination point based on a subsystem scheduled by the number of vaccines, obtain data such as the vaccination schedule at each vaccination point based on a subsystem scheduled by the vaccination time, and obtain predicted data of the number distribution of vaccines at each vaccination point in a certain period of time in the future based on a subsystem predicted by the scheduling of vaccines.
When the smart city vaccine scheduling system 200 acquires the data, a plurality of management platforms can be independently arranged corresponding to each subsystem for data collection.
For example, the smart city vaccine dispatch system 200 may set up a city infrastructure management sub-platform. The city infrastructure management sub-platform may determine the preset vaccination service range data of each vaccination point based on the initial regional plan of each vaccination point (such as each hospital and community service vaccination site) in the region, and upload the data to the city vaccine management platform 230. The city vaccine management platform 230 may adjust the service range of each vaccination point based on the preset vaccination service range data of each vaccination point, and in combination with the number of times that each vaccination point is queried on the user platform, and further, adjust the vaccine quantity arrangement of each vaccination point.
As another example, the smart city vaccine dispatch system 200 may provide a city demographic information management sub-platform. The urban population information management sub-platform can perform statistical processing based on population information in the area, for example, statistics of data such as population distribution, occupation and work information of people in various age groups in the area are performed. The city demographic information management sub-platform may also upload the statistics to the city vaccine management platform 230. The city vaccine management platform 230 may determine the timing of vaccination based on the data described above, e.g., schedule retirees and children to be vaccinated before weekdays, and schedule students and office workers to be vaccinated before weekends.
For another example, the smart city vaccine dispatch system 200 may be configured with a sub-platform for government epidemic information management. The government epidemic situation information management sub-platform can upload the epidemic situation data such as confirmed people, suspected confirmed people, severe people, asymptomatic people and the like in the area to the urban vaccine management platform 230. The urban vaccine management platform 230 can predict the number of people to be vaccinated before a certain period of time in the future based on epidemic situation data, the number of times of querying each vaccination point on the user platform, the vaccination results of each vaccination point, and the like, and determine a vaccine distribution arrangement scheme in a certain period of time in the future. The city vaccine management platform 230 uploads the prediction result of vaccine scheduling to the vaccine service platform 220, and the prediction result is uploaded to the user platform 210 by the vaccine service platform 220 and fed back to the user.
It will be apparent to those skilled in the art that, given the understanding of the principles of the system, the system may be moved to any other suitable scenario without departing from such principles.
The smart city vaccine scheduling system 200 will be described in detail below by taking the prediction scenario of the smart city vaccine scheduling system 200 applied to vaccine scheduling as an example.
The user platform 210 may refer to a platform that is dominated by the user, and includes a platform that acquires the user's needs and feeds back information to the user. In some embodiments, the user platform is configured to query the vaccination information by the user terminal inputting instructions. In some embodiments, the user platform is configured to display scheduling information for the vaccine via the display terminal.
The vaccine service platform 220 may refer to a platform that conveys the needs and control information of the user. Which connects the user platform 210 and the municipal vaccine management platform 230. In some embodiments, the vaccine service platform 220 obtains an inquiry command issued by the user through the user platform, inquires vaccination information, and feeds back the vaccination information to the user.
The municipal vaccine management platform 230 may refer to a platform for municipal vaccine scheduling. In some embodiments, the municipal vaccine management platform 230 may belong to a management platform. The city vaccine management platform 230 may be configured to obtain a service range corresponding to each vaccination point in a preset region and vaccine service related information of the service range, where the vaccine service related information includes vaccination information, target vaccinee information, and a time type of each preset time point.
In some embodiments, the city vaccine management platform 230 may be configured to determine a prediction result based on the service-wide vaccine service phase information, the prediction result comprising a number of possible vaccinations at each of the vaccination points at each of the preset time points.
In some embodiments, the municipal vaccine management platform 230 may be configured to input the vaccine service-related information for the service scope into a predictive model from which the prediction is determined.
In some embodiments, the input of the prediction model further includes popularity variation characteristics of each vaccination site, the popularity variation characteristics of each vaccination site may be dynamically changed based on variation factors, the variation factors at least include an epidemic situation and a propagation degree of each vaccination site, and the popularity variation characteristics may be obtained through a characteristic model, and the characteristic model is used for processing the epidemic situation information of each vaccination site and the information of times of being queried at a user platform to determine the popularity variation characteristics.
In some embodiments, the municipal vaccine management platform 230 may be configured to determine a distribution schedule for the vaccine based on the prediction.
In some embodiments, the urban vaccine management platform 230 may be configured to determine, based on the prediction result, the number of vaccines to be distributed and the time of vaccination for the respective vaccination site to generate the distribution scheme for the respective vaccination site; generating a vaccination schedule by the vaccine management platform based on the distribution protocol.
In some embodiments, the city vaccination management platform 230 may be configured to obtain, via the vaccination service platform 220, a vaccination schedule of the vaccination management platform 230 in response to a user querying the respective vaccination site via the user platform 210 for vaccination information, the vaccination schedule being sent via the vaccination service platform 220 to the user platform 210 for feedback to the vaccination information queried by the user.
It should be noted that the above description of the system and its components is merely for convenience of description and should not be construed as limiting the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of components or sub-systems may be combined with other components without departing from such teachings. For example, the municipal vaccine management platform and the vaccine service platform may be integrated in one component. For another example, the components may share one storage device, and each component may have a storage device. Such variations are within the scope of the present disclosure.
Fig. 3 is an exemplary flow diagram of a method for smart city vaccine scheduling in accordance with some embodiments of the present disclosure. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by the municipal vaccine management platform 230.
And 310, acquiring a service range corresponding to each vaccination point in a preset area.
In some embodiments, the municipal vaccine management platform may determine the preset area in a variety of ways. In some embodiments, the municipal vaccine management platform determines a plurality of preset areas based on administrative division. For example, the urban vaccine management platform may determine a plurality of preset areas such as a city, a de yang city, a le shan city, and the like based on the administrative division of the sichuan province. In some embodiments, the municipal vaccine management platform may determine the preset area based on user input. For example, if the user's input is within 10 kilometers of the user's current location, the city vaccine management platform may determine an area within 10 kilometers of the user's current location as a preset area.
It will be appreciated that the municipal vaccine management platform may also determine a larger or smaller predetermined area based on different needs and/or tasks, e.g., the municipal vaccine management platform may determine one or more provinces as a predetermined area. As another example, a city vaccine management platform may determine one or more streets as a predetermined area.
A vaccination site may refer to a site at which a population is vaccinated. For example, the vaccination site may be a hospital or a temporary medical service site. In some embodiments, the vaccination site may be pre-set. In some embodiments, the vaccination site may be a pre-established hospital in a pre-established area and/or a temporarily established community service vaccination site.
In some embodiments, the municipal vaccine management platform may obtain information about the vaccination site via a national and/or local government services website. For example, a municipal vaccine management platform may obtain the number of metropolitan vaccination sites and their respective locations via a metropolitan government services website.
The service range may refer to the distribution range of the population served by the vaccination site over the geographical space. For example, a vaccination site for a street may serve the administrative jurisdiction of the street.
In some embodiments, the city vaccine management platform can obtain the service range corresponding to each vaccination point in the preset area. In some embodiments, the urban vaccine management platform may determine the service ranges corresponding to the individual vaccination points based on historical data of the individual vaccination points in the preset area. For example, a city vaccine management platform may determine a distribution range of a population served by a certain vaccination point as a service range of the vaccination point based on the distribution range of the population served by the vaccination point on a geographical space in the past one year.
In some embodiments, the city vaccine management platform may determine the service ranges corresponding to the vaccination points in the preset region according to preset rules. The preset rules may be manually preset. In some embodiments, the user may flexibly set the preset rules based on different needs and/or tasks. In some embodiments, the preset rules may include preset radii, administrative jurisdictions, and the like. The following description will be given taking the preset rule as a preset radius as an example.
In some embodiments, the city vaccine management platform may modify the preset radius based on the statistical information. The statistical information may include, but is not limited to, population quantity information, vaccine quantity information, and the like. In some embodiments, the urban vaccine management platform can count the number of people served at two vaccination points in a historical time period based on the two vaccination points whose service areas are adjacent, and correct the preset radius based on the ratio of the number.
Illustratively, the service area of vaccination point a and the service area of vaccination point B are adjacent, and the initial preset radius of vaccination point a and vaccination point B are both 2 km. The urban vaccine management platform obtains the information based on statistics, the number of the population served at the vaccination point A and the vaccination point B in 1 month is 10000, the number of the population served at the vaccination point A in 2 months is 15000, the number of the population served at the vaccination point B is 5000, namely the number of the population served at the vaccination point A is increased, and the number of the population served at the vaccination point B is decreased. The urban vaccine management platform can modify the preset radius of vaccination site a for 3 months to 3 kilometers and the preset radius of vaccination site B to 1 kilometer based on the proportion of the number of populations.
In some embodiments, the urban vaccine management platform may also modify the preset radius based on the popularity variation characteristics of individual vaccination spots. For more description of the varying popularity characteristics of individual vaccination sites, reference may be made to figure 5 and its description.
In some embodiments, the city vaccine management platform may first obtain popularity variation characteristics of two adjacent vaccination points in a service area, respectively, then perform distance calculation based on basic information of the vaccination points, respectively, where the farther the distance is, the lower the popularity is represented, the smaller the preset radius should be, and finally correct the two preset radii based on a ratio between results of the distance calculation. Basic characteristics may refer to characteristics that are pre-set to represent popularity. For example, how many people are served over a period of time.
In some embodiments, the urban vaccine management platform may also modify the preset radius in other ways. For example, a resulting machine learning model is trained based on historical data, etc.
Step 320, obtaining the relevant information of the vaccine service in the service range.
The vaccine service-related information may refer to information related to a vaccine service. For example, information relating to the vaccine, information relating to the person vaccinated, etc. In some embodiments, the vaccination-service-related information may include vaccination information, target vaccinee information, time type of each preset time point.
Vaccination information may refer to information related to vaccinated and/or unvaccinated people. For example, the number of people vaccinated and unvaccinated, the occupation, age, etc. of the people vaccinated and unvaccinated. In some embodiments, vaccination information may include the number of people of various ages who have not been vaccinated.
Target vaccinee information may refer to information related to a person who has not been vaccinated. In some embodiments, the target vaccinator information may include population of various age groups and demographic flow information. E.g., the number of people aged 20 years, when and where to go to the local, etc.
The preset time point may refer to a preset time point. E.g., saturday, 9 am, etc. In some embodiments, the city vaccine management platform may determine the preset time point based on user input through the user terminal.
The time type of each preset time point may refer to a type to which the time of the preset time point belongs. In some embodiments, the city vaccine management platform may classify the time types into weekend types and weekday types. In some embodiments, the city vaccine management platform may determine monday, tuesday, wednesday, thursday, friday as the workday type. In some embodiments, the city vaccine management platform may determine saturday, sunday as weekend type.
In some embodiments, the city vaccine management platform may obtain service-wide vaccine service-related information. In some embodiments, the city vaccine management platform may obtain the vaccine service related information of the service scope based on the input of the user through the user terminal. For example, the information input by the user through the user terminal is vaccinated or unvaccinated, and the city vaccine management platform can count the number of vaccinated people and the number of unvaccinated people to obtain the vaccination information of the service range.
In step 330, a prediction result is determined based on the vaccine service-related information of the service scope.
The predicted outcome may refer to the number of people to be vaccinated in the future at each vaccination site. In some embodiments, the predicted outcome may include the number of possible vaccinations at each of the predetermined time points, each of the vaccination points. For example, the number of possible vaccinations at vaccination site a on saturday is 200, and the number of possible vaccinations on sunday is 250.
In some embodiments, the urban vaccine management platform may determine the predicted outcome based on the service-wide vaccine service-related information. In some embodiments, the urban vaccine management platform may determine the prediction result based on a comparison of the vaccine service-related information for the service scope with historical vaccine service-related information for the service scope. For example, if the urban vaccine management platform determines that the possible vaccination population of the vaccination site a on saturday is always twice as many as the possible vaccination population on monday based on the historical information on the vaccine service, the urban vaccine management platform may determine that the vaccination population of the vaccination site a on saturday of the week is 200 based on the vaccination population of the vaccination site a on monday of the week being 100.
In some embodiments, the municipal vaccine management platform may also determine the predicted outcome by a predictive model. For more description of the determination of the prediction result by the prediction model, refer to fig. 5 and its description.
Based on the prediction, a vaccine distribution schedule is determined, step 340.
The distribution schedule of the vaccine may refer to the arrangement of the vaccine distribution to the vaccination sites. In some embodiments, the distribution protocol may include scheduling of the number of vaccines, time of vaccination based on the number of possible vaccinations at each vaccination site. For example, a distribution schedule may schedule 50 vaccines for the vaccination site a monday.
In some embodiments, the municipal vaccine management platform may determine a distribution schedule for the vaccine based on the prediction results. For example, if the predicted outcome is that the possible vaccination population for vaccination site a on saturday is 200, the urban vaccine management platform may determine that the distribution schedule of the vaccine requires 400 vaccines for vaccination site a on saturday.
For more description of determining a vaccine distribution based on the prediction results, reference may be made to fig. 4 and its description.
Some embodiments of the present description obtain, by a vaccine management platform, a service range corresponding to each vaccination point in a preset region, and further obtain vaccine service related information of the service range, so that the vaccine service related information can be determined in a refined manner. The prediction result is determined based on the relevant information of the vaccine service, and then the distribution scheme of the vaccine is determined, so that the accuracy of the distribution of the vaccine can be improved.
Fig. 4 is an exemplary schematic diagram of a vaccination information query, according to some embodiments of the present description. As shown in fig. 4, the process 400 includes the following steps. In some embodiments, the flow 400 may be performed by a city vaccine management platform.
And step 410, responding to the vaccination information inquiry of the user to each vaccination point through the user platform, and acquiring the vaccination arrangement of the urban vaccine management platform through the vaccine service platform.
The vaccination schedule may refer to each of the predetermined time points, each of which schedules vaccination. For example, vaccination site a will be 200 vaccinations on saturday and 300 vaccinations on sunday. In some embodiments, the vaccination schedule is sent via the vaccination service platform to the user platform for feedback to the user of the queried vaccination information. The feedback mode includes but is not limited to short message reminding, mailbox reminding and the like.
In some embodiments, the city vaccine management platform may obtain the vaccination schedule of the city vaccine management platform in response to a user querying vaccination information for various vaccination points through the user platform. For example, as shown in fig. 4, a user may enter information and/or view information through a user platform, which may communicate with the vaccine service platform in response to information and/or instructions entered by the user. If the user inquires the vaccination information of each vaccination point through the user platform, the urban vaccine management platform can respond to the inquiry to obtain the vaccination arrangement of the urban vaccine management platform, and then the vaccination arrangement is sent to the user platform through the vaccine service platform, so that the feedback of the vaccination information inquired by the user is realized.
Step 420, determining the number of distributed vaccines and the distribution time of each vaccination point based on the prediction result so as to generate a distribution scheme of each vaccination point; based on the distribution schedule, a vaccination schedule is generated. In some embodiments, information related to vaccination schedules may be fed back to the user through the vaccine service platform and the user platform.
In some embodiments, the urban vaccine management platform may determine the number of vaccines to be distributed and the time of vaccination for each vaccination site based on the prediction results to generate a distribution plan for each vaccination site. For example, the urban vaccine management platform may determine that the number of possible vaccinations at vaccination site a on saturday is 200 based on the prediction result, that the number of vaccines distributed at vaccination site a is 200, the vaccination time is saturday, and the distribution scheme for generating vaccination site a is that vaccination site a requires 200 vaccines on saturday.
In some embodiments, the urban vaccine management platform may stagger the time of the persons to be vaccinated according to different properties of the persons based on the prediction results. Different properties of a person may include, but are not limited to, retirees, children, students, office workers, and the like. For example, the city vaccine management platform may schedule the inoculation time of retired people on monday and schedule the inoculation time of office workers on saturday and sunday based on the prediction result that the possible number of vaccinations of vaccination point a on saturday is 200, including retired people and office workers.
In some embodiments, the municipal vaccine management platform may generate a vaccination schedule based on the distribution schedule. In some embodiments, the municipal vaccine management platform may generate a vaccination schedule based on the distribution protocol through user input. For example, a user may enter their own time only on saturday and sunday via a user platform, and the city vaccine management platform may schedule the user to vaccinate at a vaccination site on saturday or sunday.
Some embodiments of the present description communicate with the urban vaccine management platform through the vaccine service platform to obtain the vaccination schedule to feed back the query of the user, which may improve the accuracy and timeliness of the obtained vaccination schedule. Meanwhile, the distribution scheme is generated through the prediction result, so that the vaccination arrangement is generated, and the accuracy of the vaccination arrangement can be improved.
It should be noted that the above description of the flow is for illustration and description only and does not limit the scope of the application of the present specification. Various modifications and alterations to the flow may occur to those skilled in the art, given the benefit of this description. However, such modifications and variations are intended to be within the scope of the present description.
FIG. 5 is an exemplary flow diagram illustrating the determination of a prediction result by a predictive model according to some embodiments of the present description. As shown in fig. 5, the process 500 includes the following steps. In some embodiments, the process 500 may be performed by a city vaccine management platform.
And step 510, inputting the relevant information of the vaccine service in the service range into a prediction model, and determining a prediction result through the prediction model.
A predictive model may refer to a model used to determine a predicted outcome. The predictive model may be a trained machine learning model. In some embodiments, the predictive model may be a deep neural network model. In some embodiments, the predictive model may include other models. For example, any one or combination of a recurrent neural network model, a convolutional neural network, or other custom model structure, etc.
In some embodiments, the city vaccine management platform can input the vaccine service related information of the service range into the prediction model, and the prediction result is determined by the prediction model.
In some embodiments, the input to the predictive model may also include information relating to the vaccination site. For example, the geographic location of the vaccination site, the maximum number of vaccinations that can be administered a day at the vaccination site, etc.
In some embodiments, the input to the predictive model may also include a popularity variation characteristic for each vaccination spot.
The popularity variation profile of each vaccination site may refer to a profile based on the variation in the amount of interest to each vaccination site over a period of time. The individual vaccination points being of interest may refer to the acceptance of service at the individual vaccination points and/or the individual vaccination points being drawn up by humans as target vaccination points.
In some embodiments, the popularity variation characteristics of individual vaccination spots may vary by other factors. Other factors may include, but are not limited to, price, population, supply-demand relationship, etc. For example, as the price of vaccine increases at vaccination site a, its popularity profile may decrease. For another example, a large portion of the population within the service range of vaccination site a has been vaccinated, and its popularity profile may be reduced.
In some embodiments, the popularity variation characteristics of each vaccination site may be dynamically varied based on varying factors including at least the epidemic situation and the extent of spread of each vaccination site.
The variable factor may refer to a factor that affects the popularity of the vaccination spot. In some embodiments, the varying factors include at least the epidemic and the extent of transmission at the various vaccination sites.
The epidemic situation at each vaccination site may refer to the development of an epidemic within the service area of the vaccination site. For example, the number of confirmed persons, the number of suspected confirmed persons, the number of severe persons, and the number of asymptomatic persons. In some embodiments, the city vaccine management platform can obtain the epidemic situation of each vaccination point through the government epidemic situation information management sub-platform.
The degree of spread may refer to the degree to which a certain vaccination site is known in a human population. In some embodiments, the degree of propagation may be represented by numbers, words, or the like. For example, the degree of spread at vaccination site a is 80%.
In some embodiments, the municipal vaccine management platform may determine the extent of dissemination based on a user's query. The user query may refer to the user querying the vaccination information at the user platform for each vaccination site. The number of queries of the user may be referred to as number-of-queries information queried at the user platform. In some embodiments, the city vaccine management platform may obtain the number of queries of the user through the user platform and determine the number of queries as the degree of dissemination of the vaccination site. For example, the city vaccine management platform initially determines the propagation degree of the vaccination point a to be 40% based on that the number of user accounts of which the user platform has obtained the inquiry about the vaccination point a is 400, the number of population in the service range targeted by the vaccination point a is 1000.
In some embodiments, the city vaccine management platform may use a plurality of labeled training samples to train through a plurality of methods (e.g., gradient descent) when training the predictive model, so that parameters of the model can be learned. And when the trained model meets the preset condition, finishing the training and obtaining the trained prediction model.
The training samples may be historical vaccine service related information for the service scope. The training labels can be corresponding historical prediction results, and the labels of the training samples can be obtained through manual labeling. In some embodiments, the predictive model may be trained in another device or module.
In some embodiments, step 510 may include a substep 511 for obtaining a popularity change characteristic.
The popularity variation characteristics may be obtained via a feature model, step 511.
A feature model may refer to a model used to determine popularity variation features. The feature model may be a trained machine learning model. In some embodiments, the predictive model may include any one or combination of a recurrent neural network model, a convolutional neural network, or other custom model structure, or the like.
In some embodiments, the signature model may process the epidemic information for each vaccination site and the number of times it is queried at the user platform to determine the popularity variation signature. For example, as shown in step 511 of fig. 5, the characteristic model may process the epidemic situation information of the vaccination site and the information of the number of times that the vaccination site is queried on the user platform, and determine the popularity variation characteristic of the vaccination site.
In some embodiments, the feature model may be obtained based on a plurality of model joint training. For example, the feature model may be obtained based on a joint training of the second initial feature model and a similarity model.
In some embodiments, in the joint training of the feature models, the training samples of the two initial feature models are based on historical sample data of the same vaccination site. In some embodiments, at least one of the two inputs to the two initial feature models is different. For example, the two initial feature models input either or both of the epidemic situation information of the same inoculation point and the queried number information of the same inoculation point.
In some embodiments, the input to the similarity model is the output of the two initial feature models, and the output of the similarity model is the vaccination site vaccination result similarity. The inoculation result can refer to the number of inoculated people in a preset time period. In some embodiments, the inoculation results may include the number of inoculations at various ages, the number of inoculations in areas at different distances from the inoculation point, and the like. It will be appreciated that the inoculation results may vary at different times. In some embodiments, the urban vaccine management platform may determine the vaccination results at different times based on the epidemic situation information of the same vaccination point at different times and the queried number information of the same vaccination point.
In some embodiments, the two initial feature models may be the same model, and the parameters of the two initial feature models may be shared. In some embodiments, the two initial feature models may be named a first initial feature model and a second initial feature model, respectively.
The first initial feature model and the second initial feature model may be trained machine learning models. In some embodiments, the first initial feature model and the second initial feature model may both be LSTM models. In some embodiments, the first initial feature model and the second initial feature model may include any one or combination of other models, such as a recurrent neural network model, a convolutional neural network, or other custom model structures, and the like.
In some embodiments, the first initial characteristic model and the second initial characteristic model may process different epidemic situation information and different query times to determine different popularity variation characteristics. For example, the first initial feature model may process the first epidemic situation information and the first queried frequency information of the vaccination site a, determine the first popularity variation feature, and may determine the first vaccination result at the same time; the second initial characteristic model can process the second epidemic situation information and the second inquired times information of the vaccination point A, determine the second popularity variation characteristic and simultaneously determine the second vaccination result.
The similarity model may be a trained machine learning model. In some embodiments, the similarity model may be a deep neural network model. In some embodiments, the similarity model may include any one or combination of other models, such as a recurrent neural network model, a convolutional neural network, or other custom model structure, and the like.
In some embodiments, the similarity model may process the first and second vaccination results to determine vaccination result similarity.
In some embodiments of the present specification, the parameters of the feature model obtained by the above training method are beneficial to solving the problem that the label is difficult to obtain when the feature model is trained alone in some cases, and the feature model can also obtain the relevant parameters that can better reflect the popularity variation feature.
Some embodiments of the present description determine the prediction result through the prediction model, which may reduce human involvement and reduce human costs. Furthermore, the popularity variation characteristics are input into the prediction model, so that the generated prediction result can be more accurate.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
In some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims (8)

1. A smart city vaccine scheduling method based on the Internet of things is characterized in that the method is executed by a city vaccine management platform and comprises the following steps:
acquiring a service range corresponding to each vaccination point in a preset area;
obtaining relevant information of the vaccine service in the service range, wherein the relevant information of the vaccine service comprises vaccination information, target vaccinator information and time types of all preset time points;
determining a prediction result based on the vaccine service related information of the service range, wherein the prediction result comprises the possible vaccination population of each vaccination point at each preset time point;
determining a dispensing regimen for the vaccine based on the prediction.
2. The method of claim 1, wherein the method further comprises:
responding to the user to inquire the vaccination information of each vaccination point through a user platform, and acquiring the vaccination arrangement of the urban vaccine management platform through a vaccine service platform, wherein the vaccination arrangement is sent to the user platform through the vaccine service platform to feed back the vaccination information inquired by the user.
3. The method of claim 1, wherein the determining a prediction result based on the vaccine service-related information for the service scope comprises:
inputting the relevant information of the vaccine service in the service range into a prediction model, and determining the prediction result through the prediction model.
4. The method of claim 3, wherein the inputs to the predictive model further include a popularity variation profile for each of the vaccination sites.
5. The method of claim 4, wherein said popularity variability characteristic of each of said vaccination sites is dynamically variable based on variability factors including at least the epidemic and the extent of spread of each of said vaccination sites.
6. The method of claim 4, wherein the popularity variation characteristics are obtained by a feature model, and the feature model is used for processing the epidemic situation information of each vaccination site and the information of the number of times of being inquired at the user platform to determine the popularity variation characteristics.
7. The method of claim 1, wherein determining a vaccine distribution regimen based on the prediction comprises:
determining the number of vaccines distributed and the time of distribution of each vaccination point based on the prediction result so as to generate the distribution scheme of each vaccination point;
generating a vaccination schedule based on the distribution protocol.
8. A smart city vaccine scheduling system based on the Internet of things, the system comprising a city vaccine management platform, a vaccine service platform and a user platform, the city vaccine management platform being configured to perform the following operations:
acquiring a service range corresponding to each vaccination point in a preset area;
acquiring relevant vaccine service information of the service range based on the service range, wherein the relevant vaccine service information comprises vaccination information, target vaccinator information and time types of all preset time points;
determining a prediction result based on the relevant information of the vaccine service in the service range, wherein the prediction result comprises the possible vaccination population of each vaccination point at each preset time point;
determining a dispensing regimen for the vaccine based on the prediction.
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